Generative models learn the underlying high-dimensional data distribution and
have achieved promising performance on image synthesis [1]}, [2]}, [3]}, [4]}.
Though being well praised, they still face two main criticisms. First, since generative models are typically trained in an unsupervised way, they lack controllability, meaning that it is unclear how to sample from a specific region of the space. Second, generative models are prone to inherit the imbalance and bias of training data [5]}, [6]}.
For instance, StyleGAN2 is more likely to produce images of white individuals, see Figure REF (e). Previous works have studied these challenges separately, and typical methods include editing of “style” codes [7]}, [8]}, [6]} and explicit conditions [10]}, [11]}. However, these methods are either model-dependent (i.e., requiring a well-structured style space) or label-intensive (i.e., requiring all training samples to be labeled for explicit conditions), limiting their generality and practical use.
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